Application of predator-prey optimization for task scheduling in cloud computing
Cloud computing environments require scheduling to allocate resources efficiently and ensure optimal performance. It is possible to maximize resource utilization and minimize execution time by scheduling cloud systems effectively. Meta-heuristic algorithms aim to address this NP-hard problem by taki...
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| Format: | Article |
| Language: | English |
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Shahid Bahonar University of Kerman
2025-01-01
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| Series: | Journal of Mahani Mathematical Research |
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| Online Access: | https://jmmrc.uk.ac.ir/article_4540_549ee6bcb468a32e0d21d2aae8348662.pdf |
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| author | Zahra Jalali Khalil Abadi Behnam Mohammad Hasani zade Najme Mansouri Mohammad Masoud Javidi |
| author_facet | Zahra Jalali Khalil Abadi Behnam Mohammad Hasani zade Najme Mansouri Mohammad Masoud Javidi |
| author_sort | Zahra Jalali Khalil Abadi |
| collection | DOAJ |
| description | Cloud computing environments require scheduling to allocate resources efficiently and ensure optimal performance. It is possible to maximize resource utilization and minimize execution time by scheduling cloud systems effectively. Meta-heuristic algorithms aim to address this NP-hard problem by taking into account these QoS parameters. In order to deal with the task scheduling problem, we utilize a new meta-heuristic algorithm known as Predator-Prey Optimization (PPO). In PPO, predators and preys are modeled and their energy gains are determined by their body mass and interactions. Faster convergence rates enhance PPO's ability to find optimal solutions. The balance between exploration and exploitation makes it suitable for solving real-world problems in unknown spaces. The PPO-based Task Scheduling algorithm (PPOTS) has the goal of reducing execution time and makespan while increasing resource utilization. In this study, the PPOTS algorithm is compared to five well-known meta-heuristic algorithms: Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Spotted Hyena Optimization Algorithm (SHO), Grasshopper Optimization Algorithm (GOA), and Sooty Tern Optimization Algorithm (STOA). Furthermore, the proposed PPOTS algorithm was compared with two new meta-heuristic based scheduling algorithms, and showed a better performance than the other two algorithms. Resource utilization and execution cost are enhanced by 8\% and 15\%, respectively, through the proposed method. |
| format | Article |
| id | doaj-art-2e80dcb13f774158980ca195093cb24f |
| institution | Kabale University |
| issn | 2251-7952 2645-4505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Shahid Bahonar University of Kerman |
| record_format | Article |
| series | Journal of Mahani Mathematical Research |
| spelling | doaj-art-2e80dcb13f774158980ca195093cb24f2025-01-04T19:30:18ZengShahid Bahonar University of KermanJournal of Mahani Mathematical Research2251-79522645-45052025-01-0114144147210.22103/jmmr.2024.22855.15714540Application of predator-prey optimization for task scheduling in cloud computingZahra Jalali Khalil Abadi0Behnam Mohammad Hasani zade1Najme Mansouri2Mohammad Masoud Javidi3Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Computer Science, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Computer Science, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Computer Science, Shahid Bahonar University of Kerman, Kerman, IranCloud computing environments require scheduling to allocate resources efficiently and ensure optimal performance. It is possible to maximize resource utilization and minimize execution time by scheduling cloud systems effectively. Meta-heuristic algorithms aim to address this NP-hard problem by taking into account these QoS parameters. In order to deal with the task scheduling problem, we utilize a new meta-heuristic algorithm known as Predator-Prey Optimization (PPO). In PPO, predators and preys are modeled and their energy gains are determined by their body mass and interactions. Faster convergence rates enhance PPO's ability to find optimal solutions. The balance between exploration and exploitation makes it suitable for solving real-world problems in unknown spaces. The PPO-based Task Scheduling algorithm (PPOTS) has the goal of reducing execution time and makespan while increasing resource utilization. In this study, the PPOTS algorithm is compared to five well-known meta-heuristic algorithms: Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Spotted Hyena Optimization Algorithm (SHO), Grasshopper Optimization Algorithm (GOA), and Sooty Tern Optimization Algorithm (STOA). Furthermore, the proposed PPOTS algorithm was compared with two new meta-heuristic based scheduling algorithms, and showed a better performance than the other two algorithms. Resource utilization and execution cost are enhanced by 8\% and 15\%, respectively, through the proposed method.https://jmmrc.uk.ac.ir/article_4540_549ee6bcb468a32e0d21d2aae8348662.pdfcloud computingtask schedulingpredator-prey optimizationmeta-heuristic |
| spellingShingle | Zahra Jalali Khalil Abadi Behnam Mohammad Hasani zade Najme Mansouri Mohammad Masoud Javidi Application of predator-prey optimization for task scheduling in cloud computing Journal of Mahani Mathematical Research cloud computing task scheduling predator-prey optimization meta-heuristic |
| title | Application of predator-prey optimization for task scheduling in cloud computing |
| title_full | Application of predator-prey optimization for task scheduling in cloud computing |
| title_fullStr | Application of predator-prey optimization for task scheduling in cloud computing |
| title_full_unstemmed | Application of predator-prey optimization for task scheduling in cloud computing |
| title_short | Application of predator-prey optimization for task scheduling in cloud computing |
| title_sort | application of predator prey optimization for task scheduling in cloud computing |
| topic | cloud computing task scheduling predator-prey optimization meta-heuristic |
| url | https://jmmrc.uk.ac.ir/article_4540_549ee6bcb468a32e0d21d2aae8348662.pdf |
| work_keys_str_mv | AT zahrajalalikhalilabadi applicationofpredatorpreyoptimizationfortaskschedulingincloudcomputing AT behnammohammadhasanizade applicationofpredatorpreyoptimizationfortaskschedulingincloudcomputing AT najmemansouri applicationofpredatorpreyoptimizationfortaskschedulingincloudcomputing AT mohammadmasoudjavidi applicationofpredatorpreyoptimizationfortaskschedulingincloudcomputing |